{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T11:17:50Z","timestamp":1770895070556,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T00:00:00Z","timestamp":1619136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fine dead fuel load is one of the most significant components of wildfires without which ignition would fail. Several studies have previously investigated 1-h fuel load using standard fuel parameters or site-specific fuel parameters estimated ad hoc for the landscape. On the one hand, these methods have a large margin of error, while on the other their production times and costs are high. In response to this gap, a set of models was developed combining multi-source remote sensing data, field data and machine learning techniques to quantitatively estimate fine dead fuel load and understand its determining factors. Therefore, the objectives of the study were to: (1) estimate 1-h fuel loads using remote sensing predictors and machine learning techniques; (2) evaluate the performance of each machine learning technique compared to traditional linear regression models; (3) assess the importance of each remote sensing predictor; and (4) map the 1-h fuel load in a pilot area of the Apulia region (southern Italy). In pursuit of the above, fine dead fuel load estimation was performed by the integration of field inventory data (251 plots), Synthetic Aperture Radar (SAR, Sentinel-1), optical (Sentinel-2), and Light Detection and Ranging (LIDAR) data applying three different algorithms: Multiple Linear regression (MLR), Random Forest (RF), and Support Vector Machine (SVM). Model performances were evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), the coefficient of determination (R2) and Pearson\u2019s correlation coefficient (r). The results showed that RF (RMSE: 0.09; MSE: 0.01; r: 0.71; R2: 0.50) had more predictive power compared to the other models, while SVM (RMSE: 0.10; MSE: 0.01; r: 0.63; R2: 0.39) and MLR (RMSE: 0.11; MSE: 0.01; r: 0.63; R2: 0.40) showed similar performances. LIDAR variables (Canopy Height Model and Canopy cover) were more important in fuel estimation than optical and radar variables. In fact, the results highlighted a positive relationship between 1-h fuel load and the presence of the tree component. Conversely, the geomorphological variables appeared to have lower predictive power. Overall, the 1-h fuel load map developed by the RF model can be a valuable tool to support decision making and can be used in regional wildfire risk management.<\/jats:p>","DOI":"10.3390\/rs13091658","type":"journal-article","created":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T02:12:57Z","timestamp":1619316777000},"page":"1658","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Machine Learning Techniques for Fine Dead Fuel Load Estimation Using Multi-Source Remote Sensing Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Marina","family":"D\u2019Este","sequence":"first","affiliation":[{"name":"Department of Agricultural and Environmental Sciences, University of Bari A. Moro, Via Amendola 165\/A, 70126 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4382-2752","authenticated-orcid":false,"given":"Mario","family":"Elia","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Environmental Sciences, University of Bari A. Moro, Via Amendola 165\/A, 70126 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9907-3730","authenticated-orcid":false,"given":"Vincenzo","family":"Giannico","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Environmental Sciences, University of Bari A. Moro, Via Amendola 165\/A, 70126 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9565-460X","authenticated-orcid":false,"given":"Giuseppina","family":"Spano","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Environmental Sciences, University of Bari A. Moro, Via Amendola 165\/A, 70126 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4642-8435","authenticated-orcid":false,"given":"Raffaele","family":"Lafortezza","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Environmental Sciences, University of Bari A. Moro, Via Amendola 165\/A, 70126 Bari, Italy"},{"name":"Department of Geography, The University of Hong Kong, Centennial Campus, Pokfulam Road, Hong Kong, China"}]},{"given":"Giovanni","family":"Sanesi","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Environmental Sciences, University of Bari A. Moro, Via Amendola 165\/A, 70126 Bari, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1016\/j.jenvman.2011.06.028","article-title":"Landscape\u2014Wildfire Interactions in Southern Europe: Implications for Landscape Management","volume":"92","author":"Moreira","year":"2011","journal-title":"J. Environ. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s00267-012-9961-z","article-title":"A Review of the Main Driving Factors of Forest Fire Ignition over Europe","volume":"51","author":"Ganteaume","year":"2013","journal-title":"Environ. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"342","DOI":"10.3832\/ifor0960-006","article-title":"Large-Scale Effects of Forest Management in Mediterranean Landscapes of Europe","volume":"6","author":"Lafortezza","year":"2013","journal-title":"IForest-Biogeosci. For."},{"key":"ref_4","unstructured":"San-Miguel-Ayanz, J., Durrant, T., Boca, R., Maianti, P., Libert\u00e0, G., Art\u00e9s-Vivancos, T., Oom, D., Branco, A., de Rigo, D., and Ferrari, D. (2020). Forest Fires in Europe, Middle East and North Africa 2019, Publications Office of the European Union."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"011001","DOI":"10.1088\/1748-9326\/ab541e","article-title":"Wildfire Management in Mediterranean-Type Regions: Paradigm Change Needed","volume":"15","author":"Moreira","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_6","unstructured":"Xanthopoulos, G., Caballero, D., Galante, M., Alexandrian, M.E., Rigolot, E., and Marzano, R. (2006). Fuels Management-How to Measure Success: Conference Proceedings."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Elia, M., Lovreglio, R., Ranieri, N., Sanesi, G., and Lafortezza, R. (2016). Cost-Effectiveness of Fuel Removals in Mediterranean Wildland-Urban Interfaces Threatened by Wildfires. Forests, 7.","DOI":"10.3390\/f7070149"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.ecolind.2014.08.034","article-title":"Prioritizing Fuel Management in Urban Interfaces Threatened by Wildfires","volume":"48","author":"Lafortezza","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.foreco.2011.04.022","article-title":"Quantifying Fine Fuel Dynamics and Structure in Dry Eucalypt Forest (Eucalyptus Marginata) in Western Australia for Fire Management","volume":"262","author":"Gould","year":"2011","journal-title":"For. Ecol. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.rse.2018.08.018","article-title":"Fuel Load Mapping in the Brazilian Cerrado in Support of Integrated Fire Management","volume":"217","author":"Franke","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.ejor.2008.05.025","article-title":"Spatial Optimization of the Pattern of Fuel Management Activities and Subsequent Effects on Simulated Wildfires","volume":"197","author":"Kim","year":"2009","journal-title":"Eur. J. Oper. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1139\/X07-077","article-title":"An Overview of the Fuel Characteristic Classification System\u2014Quantifying, Classifying, and Creating Fuelbeds for Resource PlanningThis Article Is One of a Selection of Papers Published in the Special Forum on the Fuel Characteristic Classification System","volume":"37","author":"Ottmar","year":"2007","journal-title":"Can. J. For. Res."},{"key":"ref_13","unstructured":"Rothermel, R. (1972). A Mathematical Model for Predicting Fire Spread in Wildland Fuels."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1007\/s00267-011-9707-3","article-title":"Development of Customized Fire Behavior Fuel Models for Boreal Forests of Northeastern China","volume":"48","author":"Wu","year":"2011","journal-title":"Environ. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1071\/WF11139","article-title":"Describing Wildland Surface Fuel Loading for Fire Management: A Review of Approaches, Methods and Systems","volume":"22","author":"Keane","year":"2013","journal-title":"Int. J. Wildland Fire"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1071\/WF14097","article-title":"Building Rothermel Fire Behaviour Fuel Models by Genetic Algorithm Optimisation","volume":"24","author":"Ascoli","year":"2015","journal-title":"Int. J. Wildland Fire"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"513","DOI":"10.3832\/ifor3587-013","article-title":"Harmonized Dataset of Surface Fuels under Alpine, Temperate and Mediterranean Conditions in Italy. A Synthesis Supporting Fire Management","volume":"13","author":"Ascoli","year":"2020","journal-title":"IForest-Biogeosci. For."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1007\/s00267-015-0531-z","article-title":"Developing Custom Fire Behavior Fuel Models for Mediterranean Wildland\u2013Urban Interfaces in Southern Italy","volume":"56","author":"Elia","year":"2015","journal-title":"Environ. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"37","DOI":"10.14358\/PERS.79.1.37","article-title":"Predicting Surface Fuel Models and Fuel Metrics Using Lidar and CIR Imagery in a Dense, Mountainous Forest","volume":"79","author":"Jakubowksi","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1071\/WF13086","article-title":"Estimation of Forest Structure and Canopy Fuel Parameters from Small-Footprint Full-Waveform LiDAR Data","volume":"23","author":"Hermosilla","year":"2014","journal-title":"Int. J. Wildland Fire"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lopes Queiroz, G., McDermid, G.J., Castilla, G., Linke, J., and Rahman, M.M. (2019). Mapping Coarse Woody Debris with Random Forest Classification of Centimetric Aerial Imagery. Forests, 10.","DOI":"10.3390\/f10060471"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Alonso-Rego, C., Arellano-P\u00e9rez, S., Cabo, C., Ordo\u00f1ez, C., \u00c1lvarez-Gonz\u00e1lez, J.G., D\u00edaz-Varela, R.A., and Ruiz-Gonz\u00e1lez, A.D. (2020). Estimating Fuel Loads and Structural Characteristics of Shrub Communities by Using Terrestrial Laser Scanning. Remote Sens., 12.","DOI":"10.3390\/rs12223704"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1890\/02-5145","article-title":"Mapping Fuels and Fire Regimes Using Remote Sensing, Ecosystem Simulation, and Gradient Modeling","volume":"14","author":"Rollins","year":"2004","journal-title":"Ecol. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1071\/WF9950101","article-title":"A Logit Model for Predicting the Daily Occurrence of Human Caused Forest-Fires","volume":"5","author":"Garcia","year":"1995","journal-title":"Int. J. Wildland Fire"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.foreco.2012.05.010","article-title":"Use of Random Forests for Modeling and Mapping Forest Canopy Fuels for Fire Behavior Analysis in Lassen Volcanic National Park, California, USA","volume":"279","author":"Pierce","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2019.111496","article-title":"Above-Ground Biomass Mapping in West African Dryland Forest Using Sentinel-1 and 2 Datasets - A Case Study","volume":"236","author":"Forkuor","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1109\/TGRS.2006.887002","article-title":"Estimation of Forest Fuel Load from Radar Remote Sensing","volume":"45","author":"Saatchi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2019.03.016","article-title":"Estimation of the Forest Stand Mean Height and Aboveground Biomass in Northeast China Using SAR Sentinel-1B, Multispectral Sentinel-2A, and DEM Imagery","volume":"151","author":"Liu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111341","DOI":"10.1016\/j.rse.2019.111341","article-title":"Estimating Aboveground Biomass in Subtropical Forests of China by Integrating Multisource Remote Sensing and Ground Data","volume":"232","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2006.09.032","article-title":"Remotely Sensed Measurements of Forest Structure and Fuel Loads in the Pinelands of New Jersey","volume":"108","author":"Skowronski","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lopes Queiroz, G., McDermid, G., Linke, J., Hopkinson, C., and Kariyeva, J. (2020). Estimating Coarse Woody Debris Volume Using Image Analysis and Multispectral LiDAR. Forests, 11.","DOI":"10.3390\/f11020141"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1139\/er-2020-0019","article-title":"A Review of Machine Learning Applications in Wildfire Science and Management","volume":"28","author":"Jain","year":"2020","journal-title":"Environ. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1071\/WF02024","article-title":"Assessing Canopy Fuel Stratum Characteristics in Crown Fire Prone Fuel Types of Western North America","volume":"12","author":"Cruz","year":"2003","journal-title":"Int. J. Wildland Fire"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1071\/WF19070","article-title":"Likelihood and Frequency of Recurrent Fire Ignitions in Highly Urbanised Mediterranean Landscapes","volume":"29","author":"Elia","year":"2020","journal-title":"Int. J. Wildland Fire"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1071\/WF02018","article-title":"Mediterranean Fuel Models and Potential Fire Behaviour in Greece","volume":"11","author":"Dimitrakopoulos","year":"2002","journal-title":"Int. J. Wildland Fire"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2394","DOI":"10.1139\/X08-078","article-title":"The Influence of Fuelbed Properties on Moisture Drying Rates and Timelags of Longleaf Pine Litter","volume":"38","author":"Nelson","year":"2008","journal-title":"Can. J. For. Res."},{"key":"ref_37","first-page":"2284","article-title":"Conversion of Fuel Moisture Content Values to Ignition Potential for Integrated Fire Ddanger Assessment","volume":"34","author":"Chuvieco","year":"2004","journal-title":"Agric. Sci. Collect."},{"key":"ref_38","first-page":"13","article-title":"Prediction Model of Moisture Content of Dead Fine Fuel in Forest Plantations on Maoer Mountain, Northeast China","volume":"1","author":"Masinda","year":"2021","journal-title":"J. For. Res."},{"key":"ref_39","unstructured":"Regione Puglia (2018). Compagnia Delle Foreste Boschi in Puglia, Compagnia delle Foreste. [2nd ed.]."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.eiar.2020.106474","article-title":"Estimating the Probability of Wildfire Occurrence in Mediterranean Landscapes Using Artificial Neural Networks","volume":"85","author":"Elia","year":"2020","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s13717-020-00263-4","article-title":"Modeling Fire Ignition Probability and Frequency Using Hurdle Models: A Cross-Regional Study in Southern Europe","volume":"9","author":"Ganga","year":"2020","journal-title":"Ecol. Process."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Brown, J. (1982). Handbook for Inventorying Surface Fuels and Biomass in the Interior West.","DOI":"10.2737\/INT-GTR-129"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1071\/WF08062","article-title":"A Surface Fuel Classification for Estimating Fire Effects","volume":"18","author":"Lutes","year":"2009","journal-title":"Int. J. Wildland Fire"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s10584-015-1521-0","article-title":"Fuel Moisture Sensitivity to Temperature and Precipitation: Climate Change Implications","volume":"134","author":"Flannigan","year":"2016","journal-title":"Clim. Change"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1071\/WF19061","article-title":"Estimation of Surface Dead Fine Fuel Moisture Using Automated Fuel Moisture Sticks across a Range of Forests Worldwide","volume":"29","author":"Cawson","year":"2020","journal-title":"Int. J. Wildland Fire"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features","volume":"17","author":"McFEETERS","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Giannico, V., Lafortezza, R., John, R., Sanesi, G., Pesola, L., and Chen, J. (2016). Estimating Stand Volume and Above-Ground Biomass of Urban Forests Using LiDAR. Remote Sens., 8.","DOI":"10.3390\/rs8040339"},{"key":"ref_50","unstructured":"Kuhn, M. (2021, April 22). R Foundation for Statistical Computing. Available online: https:\/\/cran.r-project.org\/web\/packages\/caret\/caret.pdf."},{"key":"ref_51","unstructured":"Breiman, L., and Cutler, A. (2021, April 22). The RandomForest Package; 15:00:24 UTC. Available online: https:\/\/cran.r-project.org\/web\/packages\/randomForest\/randomForest.pdf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support Vector Machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Appl."},{"key":"ref_53","unstructured":"Mayer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F. (2021, April 22). The E1071 Package. Available online: https:\/\/cran.r-project.org\/web\/packages\/e1071\/e1071.pdf."},{"key":"ref_54","unstructured":"Bolar, K. (2021, April 22). The Stats Package. Available online: https:\/\/cran.r-project.org\/web\/packages\/STAT\/STAT.pdf."},{"key":"ref_55","unstructured":"R Core Team (2021, April 22). R: A Language and Environment for Statistical Computing. Available online: https:\/\/www.r-project.org\/foundation\/board.html."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.envsoft.2014.03.003","article-title":"An Insight into Machine-Learning Algorithms to Model Human-Caused Wildfire Occurrence","volume":"57","author":"Rodrigues","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1080\/19475705.2016.1278404","article-title":"Modelling the Spatial Variability of Wildfire Susceptibility in Honduras Using Remote Sensing and Ge","volume":"8","author":"Valdez","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s00477-018-1558-5","article-title":"Modeling Fire Ignition Patterns in Mediterranean Urban Interfaces","volume":"33","author":"Elia","year":"2019","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_59","first-page":"87","article-title":"Stochastic Gradient Boosting Classification Trees for Forest Fuel Types Mapping through Airborne Laser Scanning and IRS LISS-III Imagery","volume":"25","author":"Chirici","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinfor."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Riley, K.L., Grenfell, I.C., Finney, M.A., and Crookston, N.L. (2014). Utilizing random forests imputation of forest plot data for landscape-level wildfire analyses. Advances in Forest Fire Research, Imprensa da Universidade de Coimbra.","DOI":"10.14195\/978-989-26-0884-6_67"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1080\/07038992.2016.1217485","article-title":"A Comparison of Machine Learning Techniques Applied to Landsat-5 TM Spectral Data for Biomass Estimation","volume":"42","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Arellano-P\u00e9rez, S., Castedo-Dorado, F., L\u00f3pez-S\u00e1nchez, C., Gonz\u00e1lez-Ferreiro, E., Yang, Z., D\u00edaz-Varela, R., \u00c1lvarez-Gonz\u00e1lez, J., Vega, J., and Ruiz-Gonz\u00e1lez, A. (2018). Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard. Remote Sens., 10.","DOI":"10.3390\/rs10101645"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.isprsjprs.2017.10.016","article-title":"Estimation and Mapping of Above-Ground Biomass of Mangrove Forests and Their Replacement Land Uses in the Philippines Using Sentinel Imagery","volume":"134","author":"Castillo","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.isprsjprs.2018.02.002","article-title":"Important LiDAR Metrics for Discriminating Forest Tree Species in Central Europe","volume":"137","author":"Shi","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Stefanidou, A., Gitas, I.Z., Korhonen, L., Georgopoulos, N., and Stavrakoudis, D. (2020). Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest. Remote Sens., 12.","DOI":"10.3390\/rs12203333"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.ecolind.2017.05.014","article-title":"Combining High-Resolution Images and LiDAR Data to Model Ecosystem Services Perception in Compact Urban Systems","volume":"96","author":"Lafortezza","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.envsoft.2017.07.007","article-title":"Development of a Predictive Model for Estimating Forest Surface Fuel Load in Australian Eucalypt Forests with LiDAR Data","volume":"97","author":"Chen","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Chuvieco, E. (2009). Estimation of Fuel Conditions for Fire Danger Assessment. Earth Observation of Wildland Fires in Mediterranean Ecosystems, Springer.","DOI":"10.1007\/978-3-642-01754-4"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13021-018-0093-5","article-title":"Estimation of Forest Aboveground Biomass and Uncertainties by Integration of Field Measurements, Airborne LiDAR, and SAR and Optical Satellite Data in Mexico","volume":"13","author":"Urbazaev","year":"2018","journal-title":"Carbon Balance Manag."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1080\/01431160500214050","article-title":"Comparative Evaluation of the Sensitivity of Multi-polarized Multi-frequency SAR Backscatter to Plant Density","volume":"27","author":"Patel","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.pce.2019.03.007","article-title":"Tree Diversity Assessment and above Ground Forests Biomass Estimation Using SAR Remote Sensing: A Case Study of Higher Altitude Vegetation of North-East Himalayas, India","volume":"111","author":"Kumar","year":"2019","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"308","DOI":"10.23953\/cloud.ijaese.201","article-title":"Risk Assessment Study of Potential Forest Fire in Idukki Wildlife Sanctuary Using RS and GIS Techniques","volume":"5","author":"Ajin","year":"2016","journal-title":"Int. J. Adv. Earth Sci. Eng."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Mancini, L., Elia, M., Barbati, A., Salvati, L., Corona, P., Lafortezza, R., and Sanesi, G. (2018). Are Wildfires Knocking on the Built-Up Areas Door?. Forests, 9.","DOI":"10.3390\/f9050234"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1007\/s10980-014-0070-7","article-title":"A Streamlined Approach for the Spatial Allocation of Fuel Removals in Wildland\u2013Urban Interfaces","volume":"29","author":"Elia","year":"2014","journal-title":"Landsc. Ecol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1073\/pnas.1315088111","article-title":"How Risk Management Can Prevent Future Wildfire Disasters in the Wildland-Urban Interface","volume":"111","author":"Calkin","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.jenvman.2018.10.027","article-title":"Towards a Comprehensive Wildfire Management Strategy for Mediterranean Areas: Framework Development and Implementation in Catalonia, Spain","volume":"231","author":"Alcasena","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1080\/19475705.2016.1155501","article-title":"Modelling Spatial Patterns of Wildfire Occurrence in South-Eastern Australia","volume":"7","author":"Zhang","year":"2016","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.agrformet.2018.06.004","article-title":"Contributions of Landscape Heterogeneity within the Footprint of Eddy-Covariance Towers to Flux Measurements","volume":"260\u2013261","author":"Giannico","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"108311","DOI":"10.1016\/j.agrformet.2020.108311","article-title":"Darker, Cooler, Wetter: Forest Understories Influence Surface Fuel Moisture","volume":"300","author":"Pickering","year":"2021","journal-title":"Agric. For. Meteorol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1658\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:52:13Z","timestamp":1760161933000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1658"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,23]]},"references-count":79,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13091658"],"URL":"https:\/\/doi.org\/10.3390\/rs13091658","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,23]]}}}